Comparison of Optical Sensor-Based Spatial Data Collection Techniques for Civil Infrastructure Modeling

Infrastructure modeling refers to the process of collecting infrastructure spatial data and transforming them into structured representations. It is useful during all stages of the infrastructure life cycle, and plays an important role in infrastructure’s development and rehabilitation applications. In order to facilitate infrastructure modeling, a variety of optical spatial data collection techniques are available. None of them is ideal for all infrastructure applications. Each has its own benefits and limitations. The main purpose of this paper is to select an appropriate technique based on the given infrastructure application requirements. To achieve this goal, the principles of these techniques are first investigated. Their benefits and limitations are identified by comparing them in aspects such as accuracy, automation of spatial data retrieval, instrument cost, and portability. This way, the relationships between techniques and the requirements of civil infrastructure applications are established and compiled in tables. Practitioners can easily select an appropriate technique for their own applications by consulting these tables.     

Integration of Construction As-Built Data Via Laser Scanning with Geotechnical Monitoring of Urban Excavation

The demand for urban underground space has been increasing in the past decades to create living space and to avoid traffic congestion. A critical concern during the design and development of the underground space is the influence of construction-related ground movements on neighboring facilities and utilities. Currently, engineers can estimate ground movements using a combination of semiempirical methods and numerical model simulation. However, these advanced analyses require accurate as-built construction staging data, which most projects lack. The traditional approach of collecting construction-staging data is both labor intensive and time consuming. This paper explores the use of three-dimensional laser scanning technology to accurately capture construction activities during development of an urban excavation. The paper describes the planning, execution, and data processing phases of collecting accurate construction as-built staging information over a period of 4?months at an urban excavation site in Evanston, Ill. The resulting data provide an unprecedented level of detail on the as-built site conditions and provide much needed information to civil engineering disciplines involved in an urban excavation including construction management and structural and geotechnical engineering.     

Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources

Awareness of the construction environment can be improved by automatic three-dimensional (3D) sensing and modeling of job sites in real time. Commercially available 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model dynamic objects in real time in an environment comprised of moving humans, equipment, and materials. Emerging prototype video range cameras offer an alternative by facilitating affordable, wide field of view, dynamic object tracking at frame rates better than 1?Hz (real time). This paper describes a methodology to model, detect, and track the position of static and moving objects in real time, based on data obtained from video range cameras. Experiments with this technology have produced results that indicate that video rate 3D data acquisition and analysis of construction environments can support effective modeling, detection, and tracking of project resources. This approach to job site awareness has inherent value and broad application. In combination with effective management practices and other sensing techniques, this technology has the potential to significantly improve safety on construction job sites.     

Construction Engineering Requirements for Integrating Laser Scanning Technology and Building Information Modeling

Laser scanning for rapid spatial data acquisition is an established technology in the architecture, engineering, and construction (AEC) sector with a wide range of applications. An understanding of the wide variation of technical requirements and considerations associated with these applications is critical to decision making about laser-scanning implementation on projects. Furthermore, significant industry transformations in the use of building information modeling present extraordinary opportunities for AEC professionals to employ the use of laser scanning in the context of holistic, collaborative workflows grounded in three-dimensional model-based design. This report analyzes the construction engineering requirements of laser scanning technology for applications across all phases of the project life cycle and proposes a multidisciplinary framework to integrate applications of laser scanning technology with the fundamentals of three-dimensional model-based design.     

Using Global Positioning System to Improve Materials-Locating Processes on Industrial Projects

On-site materials-handling operations are error prone and the errors that occur significantly decrease construction productivity. New technologies and sensing devices can enhance materials-handling management practices on construction job sites. This paper describes a study that aimed to determine the potential benefits of the deployment of global positioning system (GPS) technology within the materials-locating processes on industrial projects. Its main goals were (1) to evaluate the technical feasibility and (2) to quantify the direct benefits in terms of process duration derived from the integration of GPS devices within pipe-locating processes. A field trial was conducted and its results indicated significant time savings. This study also analyzed additional potential benefits derived from the use of GPS in this scenario.     

Optimal Planning and Scheduling for Repetitive Construction Projects

This paper presents a multiobjective optimization model for the planning and scheduling of repetitive construction projects. The model enables construction planners to generate and evaluate optimal construction plans that minimize project duration and maximize crew work continuity, simultaneously. The computations in the present model are organized in three major modules: scheduling, optimization, and ranking modules. First, the scheduling module uses a resource-driven scheduling algorithm to develop practical schedules for repetitive construction projects. Second, the optimization module utilizes multiobjective genetic algorithms to search for and identify feasible construction plans that establish optimal tradeoffs between project duration and crew work continuity. Third, the ranking module uses multiattribute utility theory to rank the generated plans in order to facilitate the selection and execution of the best overall plan for the project being considered. An application example is analyzed to illustrate the use of the model demonstrate its new capabilities in optimizing the planning and scheduling of repetitive construction projects.     

Field Experiments in Automated Monitoring of Road Construction

The growing need for better monitoring and control of road construction projects, together with rapid technological progress, have led to a number of interesting developments, which are reviewed below. The present paper describes the development of a real-time monitoring model capable of measuring productivity and progress automatically. The model, which uses global positioning system for on-site automated data collection, was tested and validated on site. The results of the field experiments have indicated that the expected accuracy level of the model can be assessed as ±4–5% for unstructured activities and even higher for more structured ones, such as asphalt spreading. The paper concludes that it is possible to automatically measure the performance of earthmoving operations. Based on the results, it also highlights the needs for further research.